We present a novel method for image enhancement of 4D
neonatal cardiac MRI using example-based
super-resolution reconstruction. Anisotropic, orthogonal
cine stacks are acquired covering the cardiac volume. By
considering small image patches within these
acquisitions, we are able to exploit the inherent
redundancy of these data. These are used to learn
coupled dictionaries of corresponding high-resolution
and low-resolution patches. These dictionaries are then
used to upsample the low-resolution view of the acquired
stack to isotropic. We apply the algorithm to
super-resolve 4D images from six neonates showing
improvement over standard bicubic interpolation.

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